Appendix D Theory and procedures
0.2 Functions
Theorem 0.2.11 Invertible is equivalent to bijective
0.6 Complex numbers
Theorem 0.6.6 Basic properties of complex arithmetic
Theorem 0.6.10 Properties of conjugation and modulus
0.7 Polynomials
Theorem 0.7.3 Basic properties of polynomials
Corollary 0.7.4 Polynomial equality via coefficients
Theorem 0.7.6 Basic properties of degree
Theorem 0.7.7 Fundamental theorem of algebra
1.1 Systems of linear equations
Theorem 1.1.13 Row equivalence theorem
1.2 Gaussian elimination
Theorem 1.2.10 Row equivalent matrix forms
1.3 Solving linear systems
Procedure 1.3.5 Solving linear systems
Corollary 1.3.10 Solutions to homogeneous equations
2.1 Matrix arithmetic
Theorem 2.1.24 Column method of matrix multiplication
Theorem 2.1.26 Row method of matrix multiplication
2.2 Matrix algebra
Theorem 2.2.1 Properties of matrix addition, multiplication and scalar multiplication
Theorem 2.2.4 Additive identities, additive inverses, and multiplicative identities
Corollary 2.2.5 Additive cancellation of matrices
Theorem 2.2.8 Matrix algebra abnormalities
Corollary 2.2.9 Failure of multiplicative cancellation
Theorem 2.2.11 Properties of matrix transposition
2.3 Invertible matrices
Theorem 2.3.2 Inverses are unique
Theorem 2.3.3 Solving with invertible matrices
Theorem 2.3.5 Inverses of \(2\times 2\) matrices
Theorem 2.3.6 Invertibility of products
Theorem 2.3.13 Properties of matrix powers
Theorem 2.3.14 Inverse and transpose
2.4 The invertibility theorem
Theorem 2.4.2 Elementary matrix formulas
Theorem 2.4.3 Inverses of elementary matrices
Theorem 2.4.5 Invertibility theorem
Theorem 2.4.9 Invertibility of triangular matrices
Theorem 2.4.10 Inverse algorithm
Theorem 2.4.11 Product of elementary matrices algorithm
Corollary 2.4.13 Left-inverse if and only if right-inverse
Corollary 2.4.14 Invertibility of product equivalence
Corollary 2.4.15 Row equivalence and invertible matrices
Corollary 2.4.17 Uniqueness of reduced row echelon form
2.5 The determinant
Theorem 2.5.5 Determinant of triangular matrices
Corollary 2.5.6 Determinant of identity matrices
Theorem 2.5.8 Expansion along rows
Theorem 2.5.9 Determinant and transposition
Corollary 2.5.10 Expansion along columns
Theorem 2.5.14 Zero rows/columns, swapping rows/columns, identical rows/columns
Theorem 2.5.17 Adjoint matrix formula
Theorem 2.5.20 Row operations and determinant
Corollary 2.5.23 Determinant and products of elementary matrices
Theorem 2.5.25 Determinant and invertibility
Theorem 2.5.26 Determinant is multiplicative
Theorem 2.5.27 Invertibility theorem (extended cut)
3.1 Real vector spaces
Theorem 3.1.13 Basic vector space properties
3.2 Linear transformations
Theorem 3.2.5 Basic properties of linear transformations
Procedure 3.2.7 The one-step linear transformation proof
Theorem 3.2.9 Matrix transformations I
Theorem 3.2.13 Rotation is a linear transformation
Theorem 3.2.17 Reflection is a linear transformation
Theorem 3.2.24 Composition of linear transformations
3.3 Subspaces
Procedure 3.3.4 Two-step proof for subspaces
Theorem 3.3.8 Intersection of subspaces
Theorem 3.3.10 Solutions to \(A\boldx=\boldzero\) form a subspace
Theorem 3.3.19 Matrix subspaces
Theorem 3.3.21 Function subspaces
Theorem 3.3.22 Polynomial equality
3.4 Null space and image
Theorem 3.4.8 Null space and image
Theorem 3.4.14 Null space and injectivity
Corollary 3.4.16 Solutions to matrix equations
3.5 Span and linear independence
Theorem 3.5.5 Spans are subspaces
Procedure 3.5.12 Investigating linear independence
Procedure 3.5.17 Investigating linear independence of functions
3.6 Bases
Theorem 3.6.7 Basis equivalence
Procedure 3.6.8 One-step technique for bases
Theorem 3.6.13 Bases and linear transformations
Corollary 3.6.16 Matrix transformations
3.7 Dimension
Theorem 3.7.3 Basis bounds
Theorem 3.7.8 Contracting and expanding to bases
Corollary 3.7.9 Street smarts
Corollary 3.7.10 Dimension of subspaces
Theorem 3.7.11 Dimension theory compendium
3.8 Rank-nullity theorem and fundamental spaces
Theorem 3.8.2 Rank-nullity
Theorem 3.8.6 Null space and image as fundamental spaces
Theorem 3.8.7 Fundamental spaces and row equivalence
Theorem 3.8.8 Fundamental spaces and row echelon forms
Theorem 3.8.9 Rank-nullity for matrices
Procedure 3.8.10 Computing bases of fundamental spaces
Theorem 3.8.12 Invertibility theorem (supersized)
Procedure 3.8.13 Contracting and extending to bases of \(\R^n\)
3.9 Isomorphisms
Theorem 3.9.4 Isomorphism equivalence
Theorem 3.9.5 Properties preserved by isomorphisms
Theorem 3.9.6 Isomorphism compendium
4.1 Inner product spaces
Theorem 4.1.3 Weighted dot product
Theorem 4.1.9 Dot product and matrix multiplication
Theorem 4.1.10 Evaluation inner products on \(P_n\)
Theorem 4.1.12 Integral inner product
Theorem 4.1.22 Basic properties of norm and distance
Theorem 4.1.23 Cauchy-Schwarz inequality
Theorem 4.1.24 Triangle Inequalities
4.2 Orthogonal bases
Theorem 4.2.2 Orthogonal implies linearly independent
Theorem 4.2.7 Calculating with orthogonal bases
Procedure 4.2.9 Gram-Schmidt procedure
Corollary 4.2.10 Existence of orthonormal bases
4.3 Orthogonal projection
Theorem 4.3.5 Orthogonal complement
Theorem 4.3.7 Fundamental spaces and orthogonal complements
Theorem 4.3.9 Orthogonal projection theorem
Corollary 4.3.14 Orthgonal projection is linear
Theorem 4.3.22 Least-squares matrix formula
5.1 Coordinate vectors
Theorem 5.1.9 Coordinate vectors for orthogonal bases
Theorem 5.1.12 Coordinate vector transformation
Procedure 5.1.14 Contracting and extending to bases in general spaces
5.2 Matrix representations of linear transformations
Theorem 5.2.3 Standard matrix as a matrix representation
Theorem 5.2.9 Computing with matrix representations
5.3 Change of basis
Theorem 5.3.2 Change of basis for coordinate vectors
Theorem 5.3.6 Change of basis matrix properties
Theorem 5.3.15 Orthogonal matrices
Theorem 5.3.17 Orthonormal change of basis
Procedure 5.3.19 Change of basis computational tips
Theorem 5.3.20 Change of basis for transformations
Procedure 5.3.23 Computing the standard matrix using change of basis
Theorem 5.3.28 Similarity and matrix representations
5.4 Eigenvectors and eigenvalues
Theorem 5.4.13 Eigenvectors of a linear transformation
Theorem 5.4.14 Eigenvectors of matrices
Corollary 5.4.16 Eigenvalues of a matrix
Procedure 5.4.19 Computing eigenspaces of a matrix
Procedure 5.4.23 Computing eigenspaces of a linear transformation
Theorem 5.4.25 Characteristic polynomial
Theorem 5.4.28 Invertibility theorem (final version)
5.5 Diagonalization
Theorem 5.5.2 Diagonalizabilty: basis of eigenvectors
Theorem 5.5.7 Linear independence of eigenvectors
Corollary 5.5.8 Diagonalizable if distinct eigenvalues
Theorem 5.5.13 Diagonalizability: dimension of eigenspaces
Procedure 5.5.14 Deciding whether a linear transformation is diagonalizable
Procedure 5.5.18 Deciding whether a matrix is diagonalizable
Corollary 5.5.20 Diagonalizabilty and similarity
Theorem 5.5.21 Properties of similarity
Theorem 5.5.23 Properties of conjugation
Theorem 5.5.30 Algebraic and geometric multiplicity
5.6 The spectral theorem
Theorem 5.6.2 Self-adjoint operators and symmetry
Corollary 5.6.3 Self-adjoint operators and symmetry
Theorem 5.6.4 Eigenvalues of self-adjoint operators
Corollary 5.6.5 Eigenvalues of self-adjoint operators
Theorem 5.6.8 Spectral theorem for self-adjoint operators
Corollary 5.6.11 Spectral theorem for symmetric matrices
Procedure 5.6.12 Orthogonal diagonalization
